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Monitoring of Winter Wheat Biomass Using UAV Hyperspectral Texture Features

  • Chang Liu
  • Guijun Yang
  • Zhenhai LiEmail author
  • Fuquan Tang
  • Haikuan Feng
  • Jianwen Wang
  • Chunlan Zhang
  • Liyan Zhang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

Biomass is an important indicator to evaluate vegetation life activities and hyperspectral imagery from unmanned aerial vehicle (UAV) supplied with abundant texture features shows a great potential to estimate crop biomass. In this paper, principal component analysis (PCA) was used to select the principal component bands from UAV hyperspectral image. Eight texture features from the principal component bands were extracted by Gray Level Co-occurrence Matrix method, and the sensitive texture features were finally selected to construct the biomass estimation model. The results show that: (1) Texture features mean, ent, sm, hom, con, dis of the first principal component (pca1) and the mean of the third principal component (pca3) were significantly correlated with the biomass. (2) The biomass model by multiple texture features (R2 = 0.654, RMSE = 0.808 (103 kg/hm2)) demonstrated better fitting effect than that by single texture feature (R2 = 0.534, RMSE = 0.960 (103 kg/hm2)). The biomass estimation model based on the texture features of multiple principal components had a good fitting effect. Therefore, texture features of the UAV platform can accurately predict the winter wheat biomass.

Keywords

Hyperspectral image Texture feature Biomass Principal component 

Notes

Acknowledgments

This study was supported in part by the National Key Technologies of Research and Development Program (2016YFD0300602) and National Natural Science Foundation of China (Grant no. 61661136003, 41601346, 41471285, 441601346).

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Chang Liu
    • 1
    • 2
    • 3
    • 4
  • Guijun Yang
    • 2
    • 3
    • 4
  • Zhenhai Li
    • 2
    • 3
    • 4
    Email author
  • Fuquan Tang
    • 1
  • Haikuan Feng
    • 2
    • 3
    • 4
  • Jianwen Wang
    • 2
    • 3
    • 4
  • Chunlan Zhang
    • 1
    • 2
    • 3
    • 4
  • Liyan Zhang
    • 2
    • 3
    • 4
  1. 1.College of GeomaticsXi’an University of Science and TechnologyXi’anChina
  2. 2.Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. ChinaBeijing Research Center for Information Technology in AgricultureBeijingChina
  3. 3.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  4. 4.Beijing Engineering Research Center for Agriculture Internet of ThingsBeijingChina

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